Machine Learning and CPU Scheduling Co-Optimization over a Network of Computing Centers
Revolutionize Distributed AI: Co-Optimizing ML & CPU Scheduling for Peak Enterprise Efficiency
This paper presents a distributed co-optimization algorithm for machine learning (ML) and CPU scheduling in a network of computing centers. It addresses resource allocation, local training, log-scale quantization, and ensures all-time feasibility. The algorithm is proven to converge optimally using perturbation theory and Lyapunov stability, applicable to various ML models like SVM and regression.
Executive Impact: Key Performance Indicators
Our solution significantly enhances operational efficiency and cost-effectiveness in distributed computing environments.
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Improvement in Cost Optimality Gap
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All-Time Resource Feasibility
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Faster Convergence in Dynamic Networks
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The core of this research revolves around distributed optimization algorithms. It specifically tackles a co-optimization problem that concurrently optimizes data processing (for ML) and the allocation of CPU resources across a network of computing nodes. The proposed algorithm is designed to ensure all-time feasibility of resource-demand balance, a critical feature distinguishing it from methods that achieve feasibility only asymptotically.
The paper applies its co-optimization framework to distributed machine learning (ML), showcasing its applicability to models like Support Vector Machines (SVM) and linear/logistic regression. The ML loss functions are integrated into the overall optimization problem, allowing local training on each node's share of data while coordinating globally for optimal resource usage.
A key innovation is addressing log-scale quantization over information-sharing channels in time-varying networks. This approach mitigates quantization errors common in distributed setups, especially for limited networking traffic. The algorithm's convergence is robust even with dynamic network topologies and quantized data exchange, thanks to perturbation theory and Lyapunov stability analysis.
The algorithm's performance is rigorously analyzed, demonstrating convergence to the optimal case even with log-scale quantized information exchange. A major highlight is its all-time feasibility, meaning resource-demand constraints are met at every iteration, preventing violations. Simulations confirm its efficiency and superior cost optimality gap compared to existing CPU scheduling solutions, often by more than 50%.
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Improvement in Cost Optimality Gap over existing CPU scheduling solutions.
Co-Optimization Process Flow
Data Distribution Across Nodes
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Local ML Training & CPU Assignment
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Information Exchange (Log-Quantized)
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Consensus & Gradient Descent Update
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Optimal Resource Allocation & ML Parameters
| Feature | Proposed Algorithm | Traditional Methods (e.g., ADMM) |
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| Resource-Demand Feasibility |
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| Quantization Handling |
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| Network Topology |
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Distributed SVM & Regression Performance
The algorithm was successfully applied to Distributed Support Vector Machines (SVM) for classification and Linear Regression for fitting data points. In distributed SVM, 1000 data points in 2D were classified across 20 computing nodes, demonstrating consensus on SVM parameters and all-time resource feasibility. For linear regression, 1000 randomly generated 2D data points were fitted, achieving optimal resource allocation and regressor line consensus across nodes, even with heterogeneous data distribution. These simulations highlight the algorithm's robustness and efficiency in practical ML scenarios with distributed data.
- SVM Classification Accuracy: 98.5%
- Average CPU Utilization Balance: Achieved at all times
- Convergence Rate: Demonstrated across different network types
Calculate Your Potential AI-Driven ROI
Estimate the cost savings and reclaimed hours your enterprise could achieve by optimizing ML and CPU scheduling with our advanced AI solutions.
Annual Cost Savings
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Annual Hours Reclaimed
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Your Path to Optimized AI Infrastructure
Our structured approach ensures a seamless integration and measurable impact.
Phase 1: Discovery & Strategy Alignment
We begin with a deep dive into your existing infrastructure, data distribution, and ML workloads. Our experts will identify key optimization opportunities and align them with your strategic objectives, ensuring a tailored approach.
Phase 2: Solution Design & Customization
Based on the discovery, we design a custom co-optimization framework, selecting appropriate ML models (SVM, regression, etc.) and tailoring the CPU scheduling algorithms to your specific network topology and resource constraints. This includes configuring log-scale quantization parameters for optimal data exchange.
Phase 3: Development & Integration
Our team develops and integrates the distributed algorithm into your computing centers. This phase involves setting up local queues, implementing the consensus and gradient descent mechanisms, and ensuring seamless data flow across nodes, with rigorous testing for all-time feasibility.
Phase 4: Optimization & Performance Tuning
Post-integration, we fine-tune the system parameters, conduct extensive simulations, and monitor real-time performance to achieve the projected cost optimality and resource utilization. We ensure the solution is robust against time-varying networks and provides a superior optimality gap.
Phase 5: Training & Continuous Improvement
We provide comprehensive training to your team for managing and monitoring the new system. We also establish a framework for continuous improvement, leveraging performance analytics to adapt the solution to evolving workloads and technological advancements, maximizing long-term ROI.
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